Robotic Subsurface Mapping Using Ground Penetrating Radar doc

Subsurface Mapping Our technical approach to the subsurface mapping problem uses a robotic mechanism tomove the sensor and automated data processing techniques to find the buried objects

Robotic Subsurface Mapping Using

Ground Penetrating Radar

Herman Herman

CMU-RI-TR-97-19

Submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Roboticss

The Robotics Institute Carnegie Mellon University

Pitsburgh, Pennsylvania 15213

May 1997

© 1997 by Herman Herman All Rights reserved.

The goal of our research is to develop an autonomous robot for subsurface mapping We aremotivated by the growing need for mapping buried pipes, hazardous waste, landmines andother buried objects Most of these are large scale mapping problems, and to manually con-struct subsurface maps in these cases would require a significant amount of resources.Therefore, automating the subsurface mapping process is an important factor in alleviatingthese problems

To achieve our goal, we have developed a robotic system that can autonomously gather andprocess Ground Penetrating Radar (GPR) data The system uses a scanning laser rangefinder

to construct an elevation map of an area By using the elevation map, a robotic manipulatorcan follow the contour of the terrain when it moves the GPR antenna during the scanningprocess The collected data are then processed to detect and locate buried objects We havedeveloped three new processing methods, two are volume based processing methods andone is a surface based processing method In volume based processing, the 3-D data aredirectly processed to find the buried objects, while in surface based processing, the 3-D dataare first reduced to a series of 2.5-D surfaces before further processing Each of these meth-ods has its own strengths and weaknesses The volume based processing methods can bemade very fast using parallel processing techniques, but they require an accurate propaga-tion velocity of the GPR signal in the soil On the other hand, the surface based processingmethod uses 3-D segmentation to recognize the shape of the buried objects, which does notrequire an accurate propagation velocity estimate Both approaches are quite efficient andwell suited for online data processing In fact, they are so efficient that the current bottleneck

in the subsurface mapping process is the data acquisition phase

The main contribution of the thesis is the development of an autonomous system for ing and localizing buried objects Specifically, we have developed three new methods to findburied objects in 3-D GPR data Using these methods, we are able to autonomously obtainsubsurface data, locate and recognize buried objects These methods differ from existingGPR data processing methods because they can autonomously extract the location, orienta-tion, and parameters of the buried object from high resolution 3-D data Most existing meth-ods only enhance the GPR data for easier interpretation by human experts There are someexisting works in automated interpretation of GPR data, but they only work with 2-D GPRdata We implemented the three different methods and also tested them by building subsur-face maps of various buried objects under different soil conditions We also used these sub-surface mapping methods to demonstrate an autonomous buried object retrieval system Insummary, we have developed a robotic system which make subsurface mapping faster, moreaccurate and reliable

Foremost I would like to thank Anthony Stentz for his guidance and support for the last 5years He was always open to my ideas, gave thoughtful comments, and asked tough ques-tions In short, Tony is a great advisor

I also would like to thank Andy Witkin for his guidance and support during the early years

of my graduate career What I learned during those years has proved to be invaluable in myresearch

Thanks to Mike Heiler for introducing me to Ground Penetrating Radar and subsurfacemapping Without his help in setting up the testbed, many experiments would not have beenpossible Thanks to James Osborn for his support and constructive comments on the subject

of subsurface mapping

Thanks to Sanjiv Singh for his collaboration on the subsurface mapping testbed The onstration of the autonomous buried object retrieval system would not have been possiblewithout his help Thanks to Mike Blackwell and Regis Hoffman for help with the laser scan-ner

dem-Thanks to Behnam Motazed, Jeffrey Daniels, Hans Moravec and Martial Hebert for ing to be on my thesis committee and for their thoughtful comments on the research

agree-Thanks to my officemates, Dave Wettergreen, Dimitrious Apostolopoulos, Mike Heiler, andSanjiv Singh for their friendship and willingness to help on various occasions

Thanks to Red Whittaker for founding the Field Robotics Center and providing the tion for many of us to do research in robotics The support of many wonderful people in thisorganization has made the research considerably better

inspira-Thanks to my father and mother None of this would have been possible without their tinuing love and support I especially thank them for believing in me and teaching me thatwith hard work, I can reach all my goals in life Thanks to my brother and sisters, Maman,Nany and Wawa, for their support and encouragement

con-Finally, I would like to thank my wife Lingga for her love and support during the past couple

of years, and Baylee for faithfully accompanying me during the long hours of work

Table of Contents

1 Robotic Subsurface Mapper 9

1.1 Introduction to Buried Object Mapping Problem 9

1.2 Research Objective 12

1.3 Technical Approach 13

1.4 Rationales 18

1.5 Applications of the Robotic Subsurface Mapper 19

2 Related Work 23

2.1 Subsurface Mapping 23

2.2 Automated Excavation and Buried Object Retrieval 26

3 Ground Penetrating Radar 29

3.1 Selection of Subsurface Sensors 29

3.2 GPR Data Collection and Data Format 37

3.3 Analysis of Different Antenna Array Configurations 39

3.4 GPR Data Visualization 43

3.5 2-D Visualization 44

3.6 3-D Visualization 50

3.7 GPR Data Processing and Interpretation 53

3.8 Example of GPR Data 55

4 Volume Based Processing 59

4.1 Overview of Volume Based GPR Data Processing 59

4.2 Background Noise Removal 60

4.3 3-D Migration Using Coherent Summation 61

4.4 3-D Migration using Reflector Pose Estimation 70

5 Surface Based Processing 95

5.1 Overview of Surface Based GPR Data Processing 95

5.2 Preprocessing 96

5.3 3-D Segmentation 102

5.4 Surface Type Categorization 105

5.5 Parameter Estimation 108

5.6 Parametric Migration 110

5.7 Propagation Velocity Computation 111

5.8 Limitation of the Surface Based Processing 112

5.9 Analysis and Result 113

6 Subsurface Mapping Testbed 127

6.1 Testbed Description 127

6.2 Software Architecture 129

6.3 An Example of Mapping and Retrieval of a Buried Object 132

7 Conclusions 135

7.1 Conclusion 135

7.2 Contribution 137

7.3 Future Direction 138

8 References 139

Chapter 1 Robotic Subsurface Mapper

1.1 Introduction to Buried Object Mapping Problem

Over the last several decades, human has buried a large amount of hazardous waste, ploded ordnance, landmines and other dangerous substances During war periods, armies ofdifferent nations have buried millions of landmines around the world A significant number

unex-of these landmines are still buried and active They annually claim a significant number unex-ofinnocent lives and maim many more people Many people and companies have also improp-erly buried hazardous toxic wastes at various sites As the containers that hold the toxicwaste age, their conditions deteriorate, necessitating the need for retrieving and movingthem to safer places United Stated also has many military training sites which contains ahuge number of unexploded ordnances These sites can not be reused for civil applicationsuntil those unexploded ordnances are removed

Finding these buried objects is hard because accurate maps, denoting where the objects areburied, rarely exist In some cases, the lack of accurate map is deliberate, such as in the case

of landmines In some other cases, the maps are accidentally lost, as in the case of some ied hazardous waste sites Even if the maps exist, vegetation or land-fill may have signifi-cantly transform the soil surface, rendering the maps unusable Regardless of the reasons,remediators now need to find the precise locations of these buried objects

bur-Finding buried objects is also important in maintaining subsurface structures We all haveheard news of utility workers accidentally cutting phone or electrical lines This happensbecause they do not know that there are buried utility structures at the excavation site Even

if a subsurface map of all buried structures exist, there is still a problem of registering theposition of a buried structure in the map with its actual location in the world It would bemuch easier if the workers could scan an area right before they excavate it and determine ifthere is any buried objects under it

These are just a few examples which illustrate the growing need to find buried man-madeobjects In some cases, remediation of these problems may also necessitate the retrieval ofthe buried objects The scale of these problems is very large We need to find and removemillions of landmines, and clean up hundreds of hazardous waste sites Presently, the con-ventional remediation process is very time consuming and prone to errors In order to under-stand the reason, let us examine how the remediation process is usually done

Currently the conventional approach to this problem involves a sequence of manual tions First, remediators scan an area using a sensor that could sense buried objects Theamount of data produced by the sensor is proportional to the size of the area If the area isquite large, the sensor can easily generate immense amount of data The sampling interval ofthe scanning process also affects the amount of data As the sampling interval gets denser,the sensor produces higher resolution data for the same area, resulting in a larger amount ofdata Experts then need to interpret the large amount of data in order to find the buriedobjects This interpretation process could easily last for several days or weeks, depending onthe size of the data Once the experts find the buried objects in the data, they need to deter-mine their actual locations in the world The process of determining the actual location of anobject from its position in the sensor data is called the registration process Depending onhow carefully the data are gathered, inaccurate registration will result in significant errors inthe locations of the objects As a result of these registration errors, the remediators wouldnot find the buried objects at the computed locations To minimize this problem, the position

opera-of the sensor during scanning is opera-often measured by surveying equipments The surveyingactivities result in a more accurate registration at the expense of longer scanning time.The above series of steps is called subsurface mapping It involves scanning an area using asensor that can sense buried objects, and finding the buried objects in the sensor data Once

we have the subsurface map, then we can start addressing the buried object retrieval process.The retrieval process also comes with its own set of problems Many of the buried objectsare hazardous to human and when they experience excessive forces, they can explode or, inthe case of hazardous waste containers, their content can leak out Heavy excavators are usu-ally used to extricate these buried objects, and they can unintentionally apply excessiveforces to the buried objects because the operator does not always know the location of the

excavator bucket with respect to the buried objects The operator knows that the objects islocated at a certain location or depth, but he/she has no information as to the relative loca-tion of the excavator bucket with respect to the buried objects As a result, only a very thinlayer of soil can be removed at a time to minimize the possibility of collision with the buriedobjects Various mechanical solutions have also been tried An example is an excavator thatuses air jet to loosen the soil material and then remove the loose material using a vacuumsuction device Such mechanical solutions only work in some situations, depending on thesoil type and the amount of soil that needs to be removed A better solution would be toinstall a positioning device to determine the location of the bucket during excavation Usingthe device, we can compute the relative position of the bucket with respect to the buriedobjects Therefore, the possibility of collision is minimized, although the human operator isstill exposed to possible danger from the buried objects.

We conclude that the current approaches to subsurface mapping and retrieval process arevery time consuming and expose the remediators to unnecessary dangers As a result, reme-diation of these buried objects is often a very costly proposition We argue that we need toautomate some of these steps Automation could both lower the cost and shorten the timeneeded to perform the necessary steps Our ultimate goal is to develop an autonomous robotthat can find and retrieve buried objects This robot would gather necessary subsurface data,interpret it, and retrieve the buried objects We envision that the robot would consists of acomputer controlled excavator, a subsurface sensor, and computers to control the mecha-nism and to interpret the sensor data Using the robot, we would eliminate the operatorexposure to danger from explosion or leaked chemical caused by collision with buriedobjects

In this thesis, we address part of the problem, which is developing a robotic subsurface per to find buried objects autonomously Specifically, we are developing algorithms that canautomatically find buried objects using Ground Penetrating Radar (GPR) data These algo-rithms are very important parts of the overall solution for autonomously finding and retriev-ing buried objects We will show the development of the algorithms and the experimentalresults which prove their feasibility

map-In the rest of this chapter we will explain the objective, technical approach, rationales andtypical applications of our research The issue of integrating subsurface mapping with bur-ied objects retrieval will also be discussed in this chapter In chapter 2, we will review exist-ing works that have been done in the area of subsurface mapping and how they relate to ourresearch In chapter 3, we will explain the principle and operation of Ground PenetratingRadar (GPR), which is the sensor that we use to sense buried objects Its limitations and dif-ferent visualization techniques for its data will also be discussed We will also examine theshortcomings of manual GPR data interpretation and the advantages of automatic GPR datainterpretation Chapter 4 and 5 will cover two different types of algorithms that we devel-

oped and implemented to automatically find buried objects in GPR data Chapter 4 explainsthe first two algorithms, which is based on 3-D voxel processing, and chapter 5 explains thethird algorithm, which is based on 3-D surface model based recognition Each of theseapproaches has its own advantages and disadvantages, and in many cases they complementeach other In each chapter, we will also present our experimental results for each algorithm

to prove the feasibility of our approaches In Chapter 6 we will show our experimental bed and explain how we use it for our experiments in subsurface mapping and buried objectretrieval Finally, chapter 7 contains our conclusions and future directions for our research

test-1.2 Research Objective

Our research objective is the following:

"Develop an intelligent robotic subsurface mapper that can autonomously detect, locate and compute geometric parameters of buried objects"

We will refer to the problem of detecting, locating and computing geometric parameters ofburied objects as the problem of "Subsurface Mapping" The geometric parameters of a bur-ied object include its size, shape, and 3-D orientation

To achieve the objective, the robotic subsurface mapper must be able to satisfy the followingrequirements:

1 Scan the soil surface and build a terrain elevation map to guide

sen-sor placement.

2 Scan the subsurface using a subsurface sensor.

3 Detect, locate and measure buried object from the subsurface sensor

data.

4 Display the subsurface data and the buried object to the operator for

notification and confirmation.

If retrieval of the buried objects is necessary then we need two additional requirements:

5 Expose the object by excavating the soil above the object.

6 Retrieve the object.

This thesis concentrates on requirement 1, 2, 3 and 4 which address the problem of face mapping for buried objects One of our success criteria is to shorten the time needed forsubsurface mapping, so the solution to these requirements should be faster than manualapproaches

subsur-We are not directly addressing the requirements 5 and 6 for retrieval of buried objects, but

we take into account the fact that the subsurface mapping process must be able to be grated well with the retrieval process In fact, we found that the ability to remove the soilabove the buried objects actually makes the subsurface mapping problem easier We willshow how we can get a more accurate subsurface map by repeatedly removing a layer of soilabove the objects and rescan the buried objects

inte-Although we are developing an autonomous system for building subsurface map It is ble to keep a human operator in the loop for safety and high-level planning Instead of gath-ering and interpreting the data, the operator acts as a supervisor for the intelligent robot Incritical operation involving unexploded ordnance retrieval, an expert operator can help guid-ing the sensing and retrieval process so the fuse of the ordnance can be located In caseswhere the system fails to correctly interpret the subsurface data or fails to plan an appropri-ate automated excavation, an expert operator can also help the system by providing addi-tional information

possi-1.3 Technical Approach

In this section, we will address our technical approach to subsurface mapping We willdescribe the sensor that we use to sense buried objects and how we process the output of thesensors to find the buried objects We also address the very important issue of integratingsubsurface mapping with buried object retrieval process by proposing a technique that can

be used to create a more accurate subsurface map during the retrieval process

1.3.1 Subsurface Mapping

Our technical approach to the subsurface mapping problem uses a robotic mechanism tomove the sensor and automated data processing techniques to find the buried objects in thesensor data In our work, the robot consists of an industrial manipulator as the scanning plat-form for the sensor, a scanning laser rangefinder to map the terrain shape, and a sensormounted at the end effector to sense buried objects First, the robot maps the terrain shapeusing its scanning laser rangefinder It uses the elevation map to guide the manipulator so itcan follow the contour of the terrain during scanning The robot also tags the sensor datawith the sensor position, so we will be able to accurately register the position of a buriedobject in the sensor data to its actual location

We use Ground Penetrating Radar (GPR) as the robot’s subsurface sensor GPR is one of themost versatile sensors for sensing buried objects It can sense nonmetallic as well as metallicobjects under various soil conditions When it scans a 2-D area, it produces a 3-D volumedata With suitable processing, this 3-D volume data can provide us with the location, orien-tation, size and shape of the buried objects Currently, most GPR data are interpreted manu-

ally by human experts They examine the data to find the buried objects, and compute theirlocation, orientation and shape This is a very time consuming process and prone to interpre-tation errors We suggest that a better solution would be to automate the interpretation pro-cess To achieve this, we have developed and implemented three new algorithms that canautomate the process of finding buried objects in GPR data, and computing their location,orientation, size and shape These algorithms are based on 3-D computer vision methods,and they reduce the GPR 3-D volume data into a few object’s parameters.

Two of these algorithms directly process the volume data to find the buried objects We callthis approach, "Volume Based Processing" To further accelerate the execution times of thealgorithms, we modified one of the algorithm so it can be run on multiple processors Due tothe local nature of the computation, the 3-D data can be split up into smaller pieces and eachpieces can be computed on different processor So by adding additional processors, we canreduce the execution time of the algorithm This is true until the number of processorsbecomes large enough that the communication between the processors become a bottleneck

In our experiment we use as many as 10 processors to run our algorithm without ing communication bottleneck

experienc-The third algorithm reduces the 3-D volume data into a series of possible objects’ surfacesand then uses model based recognition techniques to determine if any of these surfacesbelongs to a buried object We call this approach "Surface Based Processing" This approach

is much less sensitive to the problem caused by the soil inhomogeneity, since it finds theobjects by detecting their shapes The shapes appear similar under various soil conditions.Using these algorithms, along with automated data gathering, the robot can automaticallybuild the subsurface map of buried objects The steps that we describe above is illustrated inFigure 1 As shown in the figure, the subsurface map produced by our algorithms, contains

GPR Data Acquisition

Volume Based Processing:

- 3-D Segmentation

Object Surface

Mapping

Parameters:

Surface Based Processing:

Figure 1: Proposed approach for autonomous subsurface mapping

- 3-D Coherent Summation Migration

- 3-D Reflector Pose Estimation

some parameters that are previously very hard to get For example, our automated rithms can easily compute the object’s 3-D orientation from the 3-D GPR data In order toobtain the same information using manual techniques would be very time consumingbecause multiple sections of the 3-D data must be examined to compute the 3-D orientation

algo-of a buried object

1.3.2 Integration of Subsurface Mapping and Buried Object Retrieval

In some cases, subsurface mapping is not enough, we also need to retrieve the buriedobjects During the retrieval process, it is much more important to have a highly accuratesubsurface map Error in the position estimate of the object may cause collision between theexcavator bucket and the buried object The acceptable error in the position estimate of theobject depends on the distance of the excavator bucket and the buried object When theexcavator bucket is digging far away from the buried object, even a large relative error in theposition estimate on the object is acceptable As the excavator removes layers of soil abovethe object and gets closer to the object, we need to have a more accurate estimate on theposition of the object

Our solution to this problem uses repeated "Scan and Dig Cycle" During each cycle, therobot rescans the area, regenerates the subsurface map and removes a layer of soil Afterevery cycle, the robot gets closer to the buried object and there are less soil between the sen-sor and the object Since soil inhomogeneity is one of the main source of error, less soilbetween the sensor and the object translates to a smaller error in the position estimate of theobject As a result we can gradually improve our position estimate of the buried object.Figure 2 illustrates this concept The robot consists of a computer controlled excavator with

a subsurface sensor attached to its bucket It moves the bucket in order to scan an area usingthe sensor Our algorithms then process the scanned data to detect and locate the buriedobjects After an object has been located, the robot would remove a layer of soil above theobject and rescan the are to improve the estimate on the object’s location It continuallyrepeat this "Sense and Dig Cycle" until the object is very close to the surface of the soil (Fig-ure 2d) At this point it will retrieve the object

The removal of soil serves multiple purposes First, it needs to be done for the robot toretrieve the buried object Second, it enables the sensor to get a better scans of the object bygetting closer to it, thereby improving the accuracy of the subsurface map Finally, by com-paring the scans gathered before and after removal of each layer of soil, we can obtain a bet-ter estimate of the soil parameters As far as we know, this thesis is the first work whichaddresses both issues of automatically processing 3-D GPR data to find buried objects andintegrating the mapping process with the soil removal to improve the estimate on the param-eters of the buried object and soil

The actions during the sense and dig cycles can be seen in Figure 3 The main assumption ofthis approach is that the errors in the subsurface map decrease as we get closer to the buriedobjects The errors can be caused by a wrong GPR propagation velocity estimate and noisefrom spurious reflections Intuitively we can say that as the amount of soil between theantenna and the object decreases, there are fewer uncertainties in the GPR output Therefore

we should be able to get more accurate information as we get closer to the object

This approach is in contrast with existing approaches which try to obtain an accurate andhigh resolution subsurface map using a single scan These existing approaches often failbecause the soil is not homogenous, the penetration depth of the GPR signal is shallow andthe difficulty in interpreting GPR signals that are reflected from deeply buried objects Thebiggest problem with just doing a single subsurface scan in the beginning of the retrievingprocess is in obtaining an accurate position and orientation of the buried object Since theburied objects may be located at a significant distance from the surface, there are a lot ofuncertainty in the medium between the surface of the soil and the buried object This uncer-tainties cause error in the position and orientation estimate of the buried objects By doingmultiple subsurface scan each time a layer of soil above the object is removed, we can con-tinually improve the position and orientation estimate In addition, we can compute a moreaccurate parameters of the soil characteristic as we dig deeper to the soil

Target Object

Computer

Soil

Figure 2: The scenario for retrieving buried object using sense and dig cycle

Excavator bucket equipped with a subsurface sensor

a Scan the object b Remove a layer of soil

and scan the object again

c Remove another layer

of soil and scan the object

d Retrieve the objectagain

controlled

excavator

Figure 4 shows the architecture of our integrated robotic subsurface mapper and buriedobject retriever There are 4 main subsystems.First, we have the elevation map generator,which scans the ground surface to generate an elevation map The subsurface mapper usesthe elevation map to generate the path for the scanning motion of the sensor The path is exe-cuted by the robotic excavator which is equipped with a subsurface sensor at its end effector.The same robotic excavator is also used for excavating the soil.

Scan the soil surface and the subsurface volume of interest

Compute a lower bound on the distance to the closest object

Determine if the distance to the closest object is within threshold

Locate the buried objects in the 3D-data

Yes Pick Up the Object Remove a layer of soil

Figure 3: Processing steps within the sense and dig cycle

(thickness < lower bound on the distance to the closest object)

Compute propagation velocity by comparing

No Compute and update object size, shape and location parameter

Sense and

dig cycle

More Objects?

No Done Yes

Scan the soil surface and the subsurface volume of interest

Locate the buried objects in the 3D-data

the data gathered before and after the removal of soil

1.4 Rationales

Although subsurface mapping can also be done using manual methods, there are severalimportant rationales for using an autonomous or semi autonomous system to build subsur-face map They can be categorized into several different categories:

1.4.1 Improved safety

By having an autonomous system, we can remove the human operators from the operationsite, thus reducing the possible danger to the operators This is especially true for mappingsites which contain potentially explosive, radioactive or toxic materials Although the safetyproblem can also be alleviated using teleoperation, the latency and the bandwidth limitationfor low level communication between the teleoperated machine and the operator limit thetype of work that can be done Autonomous and semi autonomous systems offer much moreflexibility because the communication between the machine and the operator can happen atseveral different levels, each of which can be tailored to the task

Safety is also improved by reducing the possibility of human error in interpreting the surface sensing output and in registering the objects’ location in the subsurface map with its

sub-Robotic

2-D Laser Rangefinder and elevation map generator

Subsurface Mapper

Excavation Planner

Scanning Motion

Volume

of Soil

To Be Excavated

Dig Motion

Elevation Map

Elevation Map

Figure 4: System Architectures

Excavator

actual location in the world This is possible by using the same mechanism for mapping andexcavating, which will eliminate most of the registration error.

1.4.2 Increased productivity

A fully autonomous system could, in principle, operate continuously day or night We canalso have multiple systems operating in parallel to speed up the operation Due to theabsence of human in the operation area, fewer safety precautions need to be taken, whichshould also increase the efficiency of the retrieval task All of these factors contribute to theincrease productivity in term of man hours required for the work

1.4.3 Cost saving

Many of the applications of this work require mapping and retrieving buried objects in awide area, which could easily reach several square miles Due to the large scale of the prob-lem, any increase in productivity should result in significant saving in both time and money

We will also save quite a lot of time and money since the automated system can be operated

by operators with less expertise and skill This is possible because the difficult process ofdata interpretation and low level machine control are done autonomously by the computer.Autonomous system usually incurs a large one time cost, which is also called the non recur-ring engineering cost Once it is working, it can be duplicated at a reduced cost On the otherhand, a manual system needs experts to operate, which means that each new additional sys-tem requires training new experts

1.4.4 New capability

An integrated mapper and excavator will be able to do precise operations that is not possiblewith manually operated equipments Due to the precise information about the object’s loca-tion and orientation gathered by the mapper, the excavator will be able to excavate soil veryclose to the buried object without actually touching the object Our new improved subsur-face data processing techniques also generate the object’s location and orientation in 3-D,compared to existing techniques which mostly generates 2-D information

1.5 Applications of the Robotic Subsurface Mapper

This work can be applied to many tasks that require subsurface sensing and/or retrieval ofburied object The following are some example applications in several distinct categories:

1.5.1 Subsurface Mapping

1.5.1.1 Mapping of subsurface utility structures

For this application, the robotic mapper builds the map of subsurface structures such as gaspipes The subsurface data can be obtained by scanning in a regular grid or by tracking cer-tain subsurface features, for example by tracking the buried gas pipe individually Currentlythis is done by metal detector or by manual ground penetrating radar (GPR) operation.Metal detector does not give depth and it only works for metallic pipes Manual operation ofGPR has its own shortcomings, such as the need for expert operator and the difficulty in get-ting accurate registration between the location of the pipes in the GPR data and their actuallocations in the world It is also hard for even an expert to detect some features in the GPRdata

1.5.1.2 .Detection and mapping of unexploded ordnance and mines

A robotic subsurface mapper would be very useful in detecting and locating landmines Arobotic subsurface mapper can be deployed in advance of troops to identify a safe route.Currently landmine detection and localization are done manually using hand-held metaldetectors or mechanical probes The manual operation is very dangerous and is done at avery slow pace Using a robotic landmine mapper, the operation can be made faster by auto-mating the manual data collection and interpretation task In addition, we are not risking anyhuman life in trying to detect and locate the landmines

1.5.2 Retrieval of Buried Object

1.5.2.1 Retrieval of hazardous waste containers or unexploded ordnance

In this application, the robot needs to map the buried objects, compute their shape and tation, and generate a plan to remove them In essence, this application is a continuance ofthe detection and mapping of unexploded ordnances or mines In this application the robotdoes not stop when the subsurface objects are detected and located, but it proceeds to deter-mine their shape and orientation It uses the additional information to generate a plan toextricate or neutralize the unexploded ordnance or landmines Automated scanning andinterpretation are perfect for this application because of the reduced possible error in regis-tering the location of the object in the GPR data and its location in the real world The auto-mated scanning can also collect a very high resolution 3-D data which should increase theaccuracy of the subsurface map

orien-1.5.3 Collision prevention in excavation

1.5.3.1 Maintenance or repair of subsurface structure

In maintaining subsurface structures such as electrical lines, phone lines, or gas pipes, struction crews often need to excavate the soil around the structure In the process of doing

con-so, they sometimes hit the structure or other structures that are on their way For example: aconstruction crew from a gas company might have an accurate map of the gas pipes, but dur-ing the excavation process, the crew might hit and break an electrical line To prevent thisfrom happening, the excavator needs to know that the next volume of soil to be excavated isdevoid of any buried objects So this problem is actually a little bit simpler than the buriedobject retrieval problem, since in this application the robotic subsurface mapper only needs

to confirm that a certain volume of soil is devoid of any buried object

Chapter 2 Related Work

2.1 Subsurface Mapping

The use of subsurface sensor as a sensing modality has received very little attention in ics compared to other sensing modalities such as video images, range images or sonar.Therefore, it is not surprising to find that the proposed robotic subsurface mapper would beone of the first robotic systems to use a subsurface sensor as one of its sensing modalities Inthis case, the use of the subsurface sensor enables the robot to see through certain solidmedium, such as soil

robot-While very little work has been done in automated gathering and interpretation of face data, there have been quite a lot of work in manual subsurface data gathering and inter-pretation In the beginning, subsurface sensing is mainly used for geological explorationsand landmine detections These are done primarily using sound waves echo recorders ormetal detectors Many aspects of these two applications are at opposing extremes Geologi-cal exploration equipment uses sound waves to scan a very large area, which could easilyreach several square miles The output of the scanning operation is large and usually used tomap the macroscopic geological features On the other hand, landmine detection using ametal detector operates on a much smaller scale It is usually a point sensor that could detect

subsur-a metsubsur-al object undernesubsur-ath it The sensor size is ususubsur-ally not more thsubsur-an 1 feet in disubsur-ameter subsur-andthe output of the sensor is usually only a single value denoting the strength of the signal

from the metal detection circuit Subsequently, magnetometer is also used to detect andlocate buried objects It works by measuring the disturbance created by the buried objects onthe earth magnetic field Most recently, Ground Penetrating Radar (GPR) is also used for thedetection and localization of buried objects Lord et al [Lord 84] did a good overview ofthese various subsurface sensing techniques and their characteristics.

Among all the above sensors, GPR might be the most versatile one As a testament to itsversatility, the variety of its uses has increased significantly during recent years GPR hasbeen used in numerous diverse fields such as archaeology [Imai 87], geology [Davis 89],non-destructive testing [Beck 94][Davis 94] and engineering [Ulricksen 82] Some of thespecific tasks include mapping soil stratigraphy [Davis 89], probing underground caves[Deng 94][Vaish 94], detecting landmines [Ozdemir 92], testing roads and runways [Beck94][Davis 94][Saaraketo 94], mapping pipes and drums [Lord 84][Osumi 85][Gustafson 93]and locating persons buried under snow [Yamaguchi 91] Peters and Young [Peters 86] givegood examples of the diverse applications of GPR, while Ulricksen gave a good overview ofthe application of GPR in Civil Engineering [Ulricksen 82]

Most of the GPR data gathering and processing is currently done by manually scanning thearea of interest with a handheld antenna or antenna towed by a human-operated motorizedvehicle [Ulricksen 82][Bergstrom 93] After the data have been obtained then experts exam-ine the data to find the buried object These two operations are very time consuming andprone to many human errors After the buried object are located in the map, the real location

of the object in the world must be determined Using this information, a human operatedexcavating device can remove the soil above the objects and retrieve the buried objects.(end)

An important part in solving the detection and mapping of subsurface objects using GPR isthe understanding on how a GPR pulse travels through the soil and is reflected by buriedobjects This involves modeling the GPR system, signal propagation and reflection [Kim92] This work is important because through a better understanding of how a GPR systemworks is critical in making a better GPR system Since GPR is a form of radar, many pro-cessing techniques in radar can also be used for GPR Therefore it is important to under-stand the works that have been done in the field of radar signal processing This is especiallyimportant because conventional radar signal processing is a mature field compare to GPRsignal processing A good review of modern radar and its processing methods can be found

in the work by Eaves and Fitch [Eaves 87][Fitch 88]

As for the processing of GPR data, researchers have also experimented with multiple niques to improve GPR data Due to the similarities between GPR and seismic sensing tech-nique, there have been some efforts to apply seismic processing methods to GPR, a goodexample is the work done by Chang [Chang 89] and Fisher [Fisher 92] One disadvantage of

tech-the seismic processing technique is tech-the massive amount of required computational power Toalleviate this problem, Fiebrich [Fiebrich 87] and Fricke [Fricke 88] have implemented aseismic processing method called "Reverse Time Migration" on massively parallel super-computer Other optimization methods are also explored to reduce the needed resources inapplying the seismic processing technique [Harris 92] Due to this massive computationalrequirement and other differences between GPR and seismic [Daniels 93], seismic process-ing methods are ill-suited for realtime subsurface mapping.

Another processing technique that has been studied is the inverse scattering technique[Moghaddam 92][Oh 92] This method requires a lot of computational power because thenumber of variables that need to be solved during the inverse scattering calculation is verylarge This method also often requires a borehole antenna configuration which involves drill-ing into the ground Due to these two problems the practical uses for this method are lim-ited

There are also some works dealing with the extraction of buried object’s parameter fromGPR data Gustafson used a semi-automated method to do velocity depth inversion forcylindrical objects [Gustafson 93] He needed to select the direction of the scanning profilemanually The reflections used in the velocity-depth inversion calculation are obtained bysimple thresholding Thus, even reflections that come from non-cylindrical objects areincluded in the computation if their strength exceeds the threshold Roberts also used a semiautomated method for velocity-depth inversion to calculate the EM wave propagation veloc-ity in the soil [Roberts 93] He assumes the reflection profile comes from a point reflector.The main disadvantage is that the direction of the scanning profile needs to be manuallyselected by the operator Another work in localization of buried object is the work by Stoltewhich uses 2-D migration to highlight the location of buried pipes in the GPR data [Stolte94] In our research, we go one step further and uses high resolution 3-D data to find buriedobjects The use of 3-D data in our research eliminates the need to know the orientation ofthe buried objects in advance

These existing works have only addresses some of the issues that we need to solve, such asthe poor lateral resolution of the GPR and the huge amount of data that need to be pro-cessed As far as we know, no work has been done in the automated detection and mapping

of buried objects using high resolution 3-D GPR data

In our research, we use some of the existing methods, such as migration, in combinationwith new techniques adapted from computer vision field To the best of our knowledge weare the first to use 3-D object recognition techniques to automate the GPR data interpreta-tion process It is also important to note that all our work is performed with high resolution3-D volume data instead of 2-D data, although the methods can be modified so they can beapplied to 2-D data set as well

Recently, other researchers have begun to realize the potential for a high resolution 3-DGPR imaging [Daniels 93][Ulricksen 94] In fact, Ulricksen has built a high resolution 3-DGPR scanning mechanism which is similar in many ways to our system [Ulricksen 94] Heuses a scanning mechanism with multiple antenna configurations to obtain a high resolution3-D GPR data but he did not automate the process of buried objects detection and mapping.There have also been some efforts in automating subsurface mapping using other type ofsensors One such system is the portable pipe mapper developed at Carnegie Mellon Univer-sity by Lay et al [Lay 87] The portable pipe mapper uses electromagnetic induction tech-nique, which works well for mapping ferrous pipes, but can not be used to map other kinds

of buried objects

Although they are not directly related to subsurface mapping, ultrasound and CT scanneralso generate 3-D data which can be used to image the inside of a solid object Manyresearchers have developed methods for automatically processing the ultrasound and CTscan data [Thomas 91][Sonka 95] Similar with our GPR processing methods, these pro-cessing methods extract the object’s parameters from the 3-D data In this case, the objectcan be a blood vessel or an organ inside a human body Unfortunately, the characteristics of3-D data from ultrasound or CT scan are completely different with the characteristics of 3-DGPR data Ultrasound or CT scan data are usually gathered using multiple transducersaround an object They usually work by measuring how the signal is propagated through theobject So they do not use the reflection of the signal to image the inside of the object Onthe other hand, GPR data are usually gathered using one or two transducers In addition,these transducers can only be positioned above the surface, which limits their viewing direc-tion considerably GPR also rely on the reflected signal to detect buried objects in the soil.Due to these differences, the ultrasound and CT scan processing methods are not directlyapplicable to GPR data

2.2 Automated Excavation and Buried Object Retrieval

In the field of automatic excavation, there have been some works dealing with excavationplanning, control and soil modeling Apte discussed a representation for spatial constraints

in material removal and its application to automatic mining and lawn mowing [Apte 89].More recent work by Singh examines task planning for robotic excavation [Singh 92] Itlooks for a set of digging movements that will efficiently excavate a given volume of soilusing optimization methods In our research we concentrate just on the detection and map-ping of buried objects During our experiment of buried object retrieval using our roboticssubsurface mapper, we use the system developed by Singh [Singh 92] in controlling therobot for automated excavation

There also have been some work in automatic pipe excavation by Whittaker et al taker][Swetz 86] In this case, no subsurface sensor is employed; the retrieval is done bycarefully removing thin layers of soil until the pipe is detected by sonars This is not suitedfor sensitive and explosive objects since the objects might be struck before they are detected

[Whit-by the sonars

Chapter 3 Ground Penetrating Radar

3.1 Selection of Subsurface Sensors

There are many sensors that can be used to sense different types of buried objects Thesesensors range from a simple device such as a metal detector to a very complicated chemicaldetection device such as a Thermal Neutron Activation (TNA) sensor These sensors use dif-ferent sensing modalities which are suitable for different tasks A list of subsurface sensorsalong with their sensing modalities, advantages and disadvantages is listed in Table 9.From these various sensors, we pick GPR because it can sense metallic and nonmetallic bur-ied objects It can also obtain geometric information about the buried objects, as well as itsdepth and horizontal location GPR is also operable in propagation medium other than soil,such as fresh water [Mellet 93], which enables us to find buried objects at the bottom oflakes or rivers All of these capabilities make GPR a much more versatile sensor compared

to other subsurface sensors

3.1.1 Method of Operation

Ground Penetrating Radar (GPR) is a special radar unit that is designed to transmit magnetic pulses down to the ground instead to the air Therefore, the main difference with aconventional radar is the medium of propagation In the case of a conventional radar, the

electro-medium is the air, while in the case of GPR, the electro-medium is the soil Since soil is much moreheterogeneous and has much higher attenuation rate than air, the data generated by GPR is

of much poorer quality than the ones generated by conventional radar Due to the poorerquality data, processing the data becomes much more difficult It also accounts for the need

to have a different approach in processing GPR and conventional radar data

A block diagram of a typical impulse GPR unit is shown in Figure 5 There are four mainparts: the pulse generator, transmitting antenna, receiving antenna, and the receiver Thepulse generator produces a short pulse which is emitted by the transmitting antenna Theemitted electromagnetic pulse travels through the propagation medium until it encounters a

Ground

Penetrat-ing Radar (GPR)

• High resolution 3-D volume data output (horizontal location and depth)

• Very flexible ers configuration

transduc-• Able to detect metallic and nonmetallic objects

• Moderate cost

• Processing intensive

• Penetration depth depends on the characteristic of soil

Distance to metallic and non-metallic objects along with reflected signal inten- sity

Electromagnetic interference caused

• Low resolution due

to the long length of the signal that is used for mapping

wave-Distance to major geological features along with reflected signal intensity

informa-Chemical tion

discrimina-Table 9 Subsurface sensor technologies

discontinuity in the electrical properties of the medium The discontinuity can be caused by

an object with different electrical characteristics with the medium When the pulse hits theobject, part of its energy is reflected back to the receiving antenna The reflected electromag-netic pulse is converted by the receiving antenna to an electrical pulse The electrical pulse

is then recorded by the receiver Since it is possible to have multiple objects in front of theantennas, the receiver produces a continuous wave signal which consists of superimposedreflected pulses Figure 5 shows a separate transmitting and receiving antenna, which iscalled a bistatic configuration We could also use a single physical antenna for transmittingand receiving by connecting the antenna through a multiplexer to the pulse generator and thereceiver The single antenna configuration is called a monostatic configuration

Figure 6 shows the operation of GPR in detecting different layers of soil or buried objects.The GPR antenna is placed very close to the ground in order to achieve maximum energycoupling When the transmitted pulse hits an interface between two mediums with two dif-ferent dielectric constants, part of the energy is reflected and part of it is refracted

Pulse Generator

TransmittingAntenna

ReceivingAntennaTarget

Receiver The reflected

Figure 5 A simplified block diagram of an impulse GPR system

signal

Layer 1Material A

Layer 2Material B

Transmittedsignal

Refractedsignal

ReflectedsignalGPR Antenna

Figure 6: GPR signal reflection and refraction at the interface of two different layer of soil

The strength of the reflection and the refraction is determined by the electrical tics of the two materials The interface can be caused by different soil layers or by buriedobjects which have different dielectric constants with the soil.

characteris-For an impulse GPR, to detect and determine range to the buried object, we must detect thereflected pulse in the output of the receiver The distance is determined from the time differ-ence between the start of the transmitted pulse and the start of the reflected pulse The timedifference multiplied by the propagation velocity of the signal in the soil results in the dis-tance to the object For large objects that return strong reflections, simple thresholdingworks to detect the reflected pulse in the receiver output For weak reflections, other meth-ods must be used, which will be discussed further in later chapters

Although most of the current commercial GPR units transmit electromagnetic pulses, thereare also other types of GPR which transmit continuous wave (CW) signal or frequency mod-ulated (FM) signal In CW radar, the range is obtained from the phase difference betweenthe transmitted and reflected signals In FM radar, the range is obtained from the beat fre-quency produced when the reflected signal is mixed with the transmitted signal

Figure 7 shows an example of an impulse GPR signal consisting of the transmitted pulseemitted from the antenna and the reflected signal At the beginning of the signal, we can seethe transmitted pulse due to coupling between the transmitter and the receiver

Figure 7: An example of a GPR signal consisting of the transmitted signal and the reflected signal

Transmitted pulse Reflected signal

We sample this signal to generate its digital representation Two parameters in sampling thesignal are amplitude and time resolution Currently, we sample the amplitude of the signalevery 0.02 ns using 12 bits of data, which means that we can differentiate 4096 differentamplitude levels The 0.02 ns sampling interval translates into a spacing of 0.3 cm assuming

a vacuum medium (15 cm/ns round trip propagation velocity) In sand, the same samplinginterval translates into a 0.15cm, assuming a round trip propagation velocity of 7.5 cm/ns.Figure 8 shows a series of GPR scans of a metallic plate at various distances from theantenna As the distance between the antenna and the plate increases, the propagation timesincrease and the reflected pulses occur at later times in the reflected signal If we multiplythese propagation times with the propagation velocity, we will obtain the distances to theplate Therefore, it is important to be able to determine the start of the reflected pulse tocompute the correct propagation time It is also important to have an accurate estimate of thepropagation velocity

Another important choice to be made in determining the configuration of a GPR system iswhether to use a monostatic or bistatic configuration for the antenna A monostatic configu-ration uses a single antenna for both transmitting and receiving the electromagnetic pulse.

On the other hand, a bistatic configuration uses one antenna for transmitting the pulse andanother antenna for receiving the reflected pulse A monostatic configuration is simpler tooperate but a bistatic configuration does offer a number of advantages In a bistatic configu-ration, noise from the soil heterogeneity near the surface can be minimized This can be seen

in Figure 9 The first interface is outside the intersection of the transmitting and receiving

antennas’ beamwidth and therefore, it is not detected A section of the second interface can

be seen by both the transmitting and receiving antenna, so it can be detected So this uration could prevent spurious reflections from disturbances near the surface of the soil Onthe other hand, if the buried objects that we are interested in are located near the surface ofthe soil, a monostatic configuration is more suitable Another advantage of bistatic configu-ration is that by using several scans taken with different antenna separations, it is possible tomeasure the propagation velocity of the electromagnetic signal in the soil This is done by amethod called "Common Mid Point" (CMP) method In monostatic configuration, the prop-agation velocity must be obtained using other methods, such as using a soil’s dielectric mea-surement device The bistatic configuration can also be expanded to more than two antennas

config-Transmitting Antenna Receiving Antenna

This interface

is not detected

This interface is detectedThis is the region where

objects can be detected

Figure 9: Sensitivity region of bistatic configuration

where we have m different transmitting antennas and k different receiving antennas Using

this configuration, the CMP method can be done without moving the antennas at all because

we can place different pairs of antennas at different spacings

Bistatic configuration can also be used to collect polarimetric data In order to do this, weneed two antennas with orthogonal polarizations If the first antenna has horizontal polariza-tion and the second one has vertical polarization, we can collect data using three differentpolarizations First, we can transmit and receive using the first antenna, resulting in horizon-tal polarization Second, we can transmit and receive using the second antenna, resulting invertical polarization Finally, we can transmit using one antenna and receive using the otherone, resulting in cross polarization The polarimetric data can be used to differentiate objects

of different shape, such as a pipe and a plate, because the reflections from those objects varyunder different polarizations

Regardless of the antenna configuration, the operating frequency of the GPR must be chosencarefully, since it influences the depth of penetration, antenna size, antenna beamwidth, anddepth resolution Low frequency signals can penetrate deeper because their attenuation rate

is lower than high frequency signals The size and the beamwidth of the antenna are alsolarger for lower frequency signals In addition, low frequency signals result in lower depthresolution than high frequency signals So a low frequency GPR is suitable for detectingobjects that are buried deep in the soil On the other hand high frequency signals can notpenetrate as deeply into the soil, but offer a higher depth resolution, smaller antennas, and athinner beamwidth These characteristics make a high frequency GPR suitable for obtaininghigh resolution data of objects located close to the surface of the soil

It is also possible for a GPR system to employ multiple frequencies Such a system woulduse a low frequency signal to scan objects buried at great depths, to maximize the penetra-tion depth, and a high frequency signal to scan shallowly buried objects, to maximize theresolution This configuration is particularly suitable for buried objects retrieval becausewhen the objects are buried deep in the soil, we need to be able to detect them but we do notneed their precise locations When the objects are closer to the surface then we need theirprecise locations because we need to approach the objects carefully in order to prevent colli-sion In our testbed, we only use a high frequency GPR since we are only dealing with shal-lowly buried objects

beam-width is determined by the following equation:

Therefore, a larger antenna has a smaller beamwidth and vice versa

• Coupling efficiency is a function of antenna height When the antenna is very close

to the ground, it is coupled with the ground and the energy is transferred efficientlybetween the antenna and the ground

• Beamwidth also depends on the soil electrical characteristics This is especiallytrue when the antenna is located at some distance from the soil At the air andground interface, the beam will be refracted Characteristics of the soil determinethe angle of refraction A large angle of refraction will make the beam thinnerwhile a small angle of refraction will keep the beam width more or less the same

• At an interface between two different materials, the GPR signal experiences tion and reflection The strength and angle of the refraction and reflection depend

refrac-on the incoming angle of the signal and the characteristic of the material Theincoming and the outgoing beam angle follows the Snell equation:

• The reflection strength at each interface also depends on the incoming beam angleand the characteristic of the two bordering materials

3.1.3 Factors influencing GPR operation

3.1.3.1 Soil type

The depth of signal penetration depends on the electrical characteristics of the soil, such asattenuation rate, permeability, conductivity and dielectric constant The heterogeneity of thesoil also influences the quality of the image Various types of soil have different electricalproperties For example sand has a very low permittivity, resulting in a low attenuation ratefor electromagnetic wave in GPR frequency Clay on the other hand causes a very high

N 1 = material 1 refraction index

N 2 = material 2 refraction index

α = incoming beam angle

β = refracted beam angle

(2)

attenuation rate of the GPR signal As a result, GPR signal can not penetrate too deep in thesoil where clay concentration is high.

3.1.3.2 Soil Moisture Content

Soil moisture content influences the electrical characteristics of the soil Usually it increasesthe attenuation rate and in some cases a layer of soil with a very high moisture content cancause a strong reflection of the GPR signal

3.1.3.3 Equipment parameters

GPR signal must have parameters that are suited to the task These parameters include quency of the signal, power, sampling rate, sampling resolution, antenna configuration andsignal to noise ratio of the system

fre-3.1.3.4 Antenna to ground coupling

The height of the antenna, the orientation of the antenna and the shape of the soil under theantenna all influence the coupling of the electromagnetic energy from the antenna to thesoil For the deepest penetration depth, the amount of energy that is coupled to the groundmust be maximized In order to get maximum energy coupling between the antenna theantenna must be pointing normal to the ground plane and located very close to the ground.3.1.3.5 Antenna characteristics

Included in this category is antenna shape and other electrical characteristics Depending onthe shape we can have different radiation patterns which affect antenna gain For example, ahorn is usually used if the antenna must be placed at some distance from the ground, such as

in the case of high speed highway inspection Another example is the parabolic antenna thatcan detect objects buried at some distance from the antenna [Peters 93]

3.2 GPR Data Collection and Data Format

The basic GPR output is a 1-D time varying signal from the GPR receiver The amplitude ofthe 1-D signal is the strength of the reflected signal at that point To obtain 2-D and 3-D data,the antenna must be scanned above the area of interest in one or two directions Usually wemove the antenna in a raster scanning pattern In a typical manual GPR survey, the scan den-sity is often much higher in one direction than the other direction Our work, on the otherhand, relies on the fact that we can obtain a dense 3-D volume data by scanning the antenna

in a raster scanning pattern using very fine spacings in both directions as shown in Figure

10 This is possible because our GPR antenna is mounted on a highly accurate robotic ning mechanism This 3-D data are stored as a 3-D array where the value of the voxel is the

scan-signal amplitude at that location Each column of the 3-D grid is a single GPR scan asshown in Figure 7 The high density of the 3-D scans produce significant amount of 3-D vol-ume data If the antenna is scan using 2 cm grid, then we have 2500 scans for each squaremeter If the number of samples in the depth direction is 500, it means we have 1.25 millionvalues to consider for each square meter Due to the large amount of data, the efficiency ofthe processing algorithms is crucial to their applicability to real world problems This alsounderscores the need for automated processing of the data because it is extremely time con-suming or impossible for a human to go through the massive amount of data that is gener-ated by the GPR system.With automated and efficient processing of the data, it is feasible toprocess a high density 3-D data in real time By real time, we mean that the processing unitsare able to process the data as fast as the GPR system can generate the scans.

Figure 10: A 2-D scan above the buried object to obtain a 3-D volume data of the buried object

GPR Antenna

The raster scanning pattern of the antenna

Scanline Direction

3.3 Analysis of Different Antenna Array Configurations

In this section we will analyze the characteristics of different antenna array configurations.,which is important to determine the best antenna array for a certain task Since we are inter-ested in finding shallowly buried objects as well as deeply ones, we only considers mono-static antenna arrays Each array configuration has a different data acquisition rate, cost andcomplexity To have a valid comparison, we assume that each antenna in the array is hooked

up to a similar GPR system For the maximum sample rate of each GPR system, we use thefigure for a GSSI SIR-2 GPR system, which is about 200 scans per second

3.3.1 Single antenna

In this configuration we have a single antenna, which must be mounted on a scanning anism in order to scan a 2-D area The scanning mechanism could take a form of a mobileplatform as shown in Figure 11, which also shows the scanning pattern of the antenna Usu-

mech-ally the speed of the mobile platform is the limiting factor in this configuration The timethat it takes to scan using this configuration is determined using the following equations:

Notice that the bottleneck depends on the GPR equipment sample rate or vehicle velocity.This configuration is not suitable for scanning a very dense grid because a very accuratevehicle positioning system is needed The maximum absolute error of such positioning sys-

Antenna

VehicleScanning Direction

Figure 11: Scanning pattern for a single antenna configuration.

Sample Rate

- Scanning Area

Grid Spacing -

×

=Sample Rate=Min GPR Sample Rate,Vehicle Velocity/Grid Spacing( )

(3)

tem should be in the order of the grid spacing Anything more than that will decrease theaccuracy of the scans significantly.

Instead of mounting the antenna on a vehicle as shown in the Figure 11, we can also mountthe antenna at the end of a robotic manipulator or excavator This is especially suited forscanning a limited area, which could be reach completely by the manipulator A single axis

or a two axis scanning mechanism can be used as shown in Figure 12

A specially designed vehicle with manipulator can be used, but a computer controlled vator is also suited for the application It is also possible to retrofit existing excavator as ascanning mechanism by equipping it with a precise positioning sensor

exca-With respect to the GPR equipment, this configuration has the minimum cost, we only need

a single GPR system with a single antenna It is also the best configuration for uneven face since the height of the antenna can be adjusted for every single scan

sur-3.3.2 Linear Antenna Array

In order to minimize data acquisition time we can use a linear array of antenna as shown inFigure 13 By having multiple antennas we can decrease the amount of scanning that weneed to do The linear antenna array can be connected to a single or multiple GPR systemusing multiplexers Even when they are connected to a single GPR system, this antenna con-figuration might still have a higher data acquisition rate than the single antenna system,because it eliminates the physical scanning motion in one axis This configuration is wellsuited for scanning a continuous strip of limited width Mine detection is the obvious appli-cation, but it is also suited for pipe mapping Instead of mapping the pipes by scanning alarge area, we can also map the pipes by tracking the pipes once they are detected Once apipe is detected, the linear antenna array enables the system to get the pipe reflection profile

VehicleScanning Direction

Figure 12: Scanning pattern for a single antenna with a one axis (left) or two axis (right) scanning

Antenna ScanningAntenna

Mechanism